.png){kind=icon}
Return
Learning Human Activity From Visual Data Using Deep Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3099567.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Advances in wearable technologies have the ability to revolutionize and improve people's lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers' bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.
Keywords:
Sensors
Visualization
Activity recognition
Feature extraction
Cameras
Optical sensors
Optical network units
Human activity recognition
deep learning
first-person vision
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveWearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.png)
SENSORSOAAI
SENSORSIF3.5
OAAIShareSaveA water-stable photochromic MOF with controllable iodine sorption and efficient removal of dichromateCrystEngComm
CrystEngCommIF0
PREAI
ShareSaveShareSave
View more